\(~~~~~~~~\)Data Structure in R: XTS\(~~~~~~~~\)

Asst. Prof. Dr. Somsak Chanaim

International College of Digital Innovation, CMU

August 10, 2026

Warning

We cannot use the quantmod package to download data from Yahoo Finance in WebR.

Yahoo Finance has restricted public API access, and WebR does not allow direct internet connections.

Therefore, we use synthetic OHLC data generated within R to demonstrate xts and time-series operations.

Why we need quantmod

Download daily price of AAPL from Yahoo finance, from \(~~\)1 Jan 2020 to 30 Sep 2024, and upload to Rstudio and make this plot

Quantitative Financial Modelling Framework (quantmod)

The quantmod is useful for quantitative financial modelling framework and have three goals:

• download data

• charting

• technical indicator

install.packages("quantmod")
library(quantmod)

Download Data

getSymbols() function

getSymbols: The function for load and manage data from multiple source

Recommend the arguments

• Symbols : vector of stock or asset name

• src : “yahoo”, FRED” , etc (this course use yahoo)

• periodicity : “daily”, “weekly”, or monthly”

• from : Starting day: Year-month-day (2020-11-31)

• to : Ending day: Year-month-day (2022-12-31)

The object from yahoo finance is XTS.

Example

library(quantmod)

o show that we cannot download data from Yahoo Finance using WebR.

getSymbols(Symbols = "AAPL",
           src = "yahoo",         
           periodicity = "daily",# "weekly", "monthly"
           from = "2020-01-01",  # Year-month-day
           to = "2024-09-30")    # Year-month-day

[1] "AAPL"

or download multiple stocks price

getSymbols(Symbols = c("MSFT","META"),
           src = "yahoo",         
           periodicity = "weekly",# "weekly", "monthly"
           from = "2020-01-01",   # Year-month-day
           to = "2024-09-30")     # Year-month-day

[1] "MSFT" "META"

The xts package

eXtensible Time Series (xts)

• eXtensible Time Series (xts) is a powerful package that provides an extensible time series class, enabling uniform handling of many R time series classes by extending zoo.

• The main benefit of using xts is the compatibility with other packages that use different time-series classes (timeSeries, zoo, …).

library(xts)

Selecting, Subsetting and Indexing

We have 3 xts objects, AAPL, META, and MSFT

load("file/Data.RData")
ls()

[1] "AAPL" "META" "MSFT"

Data Frame command

head(AAPL)

           AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
2020-01-02   74.0600   75.1500  73.7975    75.0875   135480400      72.87610
2020-01-03   74.2875   75.1450  74.1250    74.3575   146322800      72.16759
2020-01-06   73.4475   74.9900  73.1875    74.9500   118387200      72.74265
2020-01-07   74.9600   75.2250  74.3700    74.5975   108872000      72.40054
2020-01-08   74.2900   76.1100  74.2900    75.7975   132079200      73.56520
2020-01-09   76.8100   77.6075  76.5500    77.4075   170108400      75.12778

Warning

In this study, We don’t need the variables AAPL.Volume and AAPL.Adjusted

AAPL <- AAPL[, 1:4]
head(AAPL)

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2020-01-02   74.0600   75.1500  73.7975    75.0875
2020-01-03   74.2875   75.1450  74.1250    74.3575
2020-01-06   73.4475   74.9900  73.1875    74.9500
2020-01-07   74.9600   75.2250  74.3700    74.5975
2020-01-08   74.2900   76.1100  74.2900    75.7975
2020-01-09   76.8100   77.6075  76.5500    77.4075

Selecting data by year

AAPL["2023"]

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2023-01-03    130.28    130.90   124.17     125.07
2023-01-04    126.89    128.66   125.08     126.36
2023-01-05    127.13    127.77   124.76     125.02
2023-01-06    126.01    130.29   124.89     129.62
2023-01-09    130.47    133.41   129.89     130.15
2023-01-10    130.26    131.26   128.12     130.73
2023-01-11    131.25    133.51   130.46     133.49
2023-01-12    133.88    134.26   131.44     133.41
2023-01-13    132.03    134.92   131.66     134.76
2023-01-17    134.83    137.29   134.13     135.94
       ...                                        
2023-12-15    197.53    198.40   197.00     197.57
2023-12-18    196.09    196.63   194.39     195.89
2023-12-19    196.16    196.95   195.89     196.94
2023-12-20    196.90    197.68   194.83     194.83
2023-12-21    196.10    197.08   193.50     194.68
2023-12-22    195.18    195.41   192.97     193.60
2023-12-26    193.61    193.89   192.83     193.05
2023-12-27    192.49    193.50   191.09     193.15
2023-12-28    194.14    194.66   193.17     193.58
2023-12-29    193.90    194.40   191.73     192.53

Selecting data by year and month

# 2022-12/2023-01
AAPL["2022-12/2023-01"]

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2022-12-01    148.21    149.13   146.61     148.31
2022-12-02    145.96    148.00   145.65     147.81
2022-12-05    147.77    150.92   145.77     146.63
2022-12-06    147.07    147.30   141.92     142.91
2022-12-07    142.19    143.37   140.00     140.94
2022-12-08    142.36    143.52   141.10     142.65
2022-12-09    142.34    145.57   140.90     142.16
2022-12-12    142.70    144.50   141.06     144.49
2022-12-13    149.50    149.97   144.24     145.47
2022-12-14    145.35    146.66   141.16     143.21
2022-12-15    141.11    141.80   136.03     136.50
2022-12-16    136.69    137.65   133.73     134.51
2022-12-19    135.11    135.20   131.32     132.37
2022-12-20    131.39    133.25   129.89     132.30
2022-12-21    132.98    136.81   132.75     135.45
2022-12-22    134.35    134.56   130.30     132.23
2022-12-23    130.92    132.42   129.64     131.86
2022-12-27    131.38    131.41   128.72     130.03
2022-12-28    129.67    131.03   125.87     126.04
2022-12-29    127.99    130.48   127.73     129.61
2022-12-30    128.41    129.95   127.43     129.93
2023-01-03    130.28    130.90   124.17     125.07
2023-01-04    126.89    128.66   125.08     126.36
2023-01-05    127.13    127.77   124.76     125.02
2023-01-06    126.01    130.29   124.89     129.62
2023-01-09    130.47    133.41   129.89     130.15
2023-01-10    130.26    131.26   128.12     130.73
2023-01-11    131.25    133.51   130.46     133.49
2023-01-12    133.88    134.26   131.44     133.41
2023-01-13    132.03    134.92   131.66     134.76
2023-01-17    134.83    137.29   134.13     135.94
2023-01-18    136.82    138.61   135.03     135.21
2023-01-19    134.08    136.25   133.77     135.27
2023-01-20    135.28    138.02   134.22     137.87
2023-01-23    138.12    143.32   137.90     141.11
2023-01-24    140.31    143.16   140.30     142.53
2023-01-25    140.89    142.43   138.81     141.86
2023-01-26    143.17    144.25   141.90     143.96
2023-01-27    143.16    147.23   143.08     145.93
2023-01-30    144.96    145.55   142.85     143.00
2023-01-31    142.70    144.34   142.28     144.29

Selecting data from starting date to any date

# start to 2022-06-05
AAPL["/2022-06-05"]

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2020-01-02   74.0600   75.1500  73.7975    75.0875
2020-01-03   74.2875   75.1450  74.1250    74.3575
2020-01-06   73.4475   74.9900  73.1875    74.9500
2020-01-07   74.9600   75.2250  74.3700    74.5975
2020-01-08   74.2900   76.1100  74.2900    75.7975
2020-01-09   76.8100   77.6075  76.5500    77.4075
2020-01-10   77.6500   78.1675  77.0625    77.5825
2020-01-13   77.9100   79.2675  77.7875    79.2400
2020-01-14   79.1750   79.3925  78.0425    78.1700
2020-01-15   77.9625   78.8750  77.3875    77.8350
       ...                                        
2022-05-20  139.0900  140.7000 132.6100   137.5900
2022-05-23  137.7900  143.2600 137.6500   143.1100
2022-05-24  140.8100  141.9700 137.3300   140.3600
2022-05-25  138.4300  141.7900 138.3400   140.5200
2022-05-26  137.3900  144.3400 137.1400   143.7800
2022-05-27  145.3900  149.6800 145.2600   149.6400
2022-05-31  149.0700  150.6600 146.8400   148.8400
2022-06-01  149.9000  151.7400 147.6800   148.7100
2022-06-02  147.8300  151.2700 146.8600   151.2100
2022-06-03  146.9000  147.9700 144.4600   145.3800

Selecting data from any date to last date

# 2023-02  to last date
AAPL["2023-02/"]

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2023-02-01    143.97    146.61   141.32     145.43
2023-02-02    148.90    151.18   148.17     150.82
2023-02-03    148.03    157.38   147.83     154.50
2023-02-06    152.57    153.10   150.78     151.73
2023-02-07    150.64    155.23   150.64     154.65
2023-02-08    153.88    154.58   151.17     151.92
2023-02-09    153.78    154.33   150.42     150.87
2023-02-10    149.46    151.34   149.22     151.01
2023-02-13    150.95    154.26   150.92     153.85
2023-02-14    152.12    153.77   150.86     153.20
       ...                                        
2024-09-16    216.54    217.22   213.92     216.32
2024-09-17    215.75    216.90   214.50     216.79
2024-09-18    217.55    222.71   217.54     220.69
2024-09-19    224.99    229.82   224.63     228.87
2024-09-20    229.97    233.09   227.62     228.20
2024-09-23    227.34    229.45   225.81     226.47
2024-09-24    228.65    229.35   225.73     227.37
2024-09-25    224.93    227.29   224.02     226.37
2024-09-26    227.30    228.50   225.41     227.52
2024-09-27    228.46    229.52   227.30     227.79

Selecting data for the first n weeks

# select the first week
first(AAPL,"1 week")

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2020-01-02   74.0600    75.150  73.7975    75.0875
2020-01-03   74.2875    75.145  74.1250    74.3575

# select the first 3 weeks
first(AAPL,"3 week")

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2020-01-02   74.0600   75.1500  73.7975    75.0875
2020-01-03   74.2875   75.1450  74.1250    74.3575
2020-01-06   73.4475   74.9900  73.1875    74.9500
2020-01-07   74.9600   75.2250  74.3700    74.5975
2020-01-08   74.2900   76.1100  74.2900    75.7975
2020-01-09   76.8100   77.6075  76.5500    77.4075
2020-01-10   77.6500   78.1675  77.0625    77.5825
2020-01-13   77.9100   79.2675  77.7875    79.2400
2020-01-14   79.1750   79.3925  78.0425    78.1700
2020-01-15   77.9625   78.8750  77.3875    77.8350
2020-01-16   78.3975   78.9250  78.0225    78.8100
2020-01-17   79.0675   79.6850  78.7500    79.6825

Selecting data for the first n days

# select the first day
first(AAPL,"1 day")

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2020-01-02     74.06     75.15  73.7975    75.0875

# select the first  3 days
first(AAPL,"3 day")

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2020-01-02   74.0600    75.150  73.7975    75.0875
2020-01-03   74.2875    75.145  74.1250    74.3575
2020-01-06   73.4475    74.990  73.1875    74.9500

Selecting data for the last n weeks

# select the last week
last(AAPL,"1 week")

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2024-09-23    227.34    229.45   225.81     226.47
2024-09-24    228.65    229.35   225.73     227.37
2024-09-25    224.93    227.29   224.02     226.37
2024-09-26    227.30    228.50   225.41     227.52
2024-09-27    228.46    229.52   227.30     227.79

# select the last 2 weeks
last(AAPL,"2 week")

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2024-09-16    216.54    217.22   213.92     216.32
2024-09-17    215.75    216.90   214.50     216.79
2024-09-18    217.55    222.71   217.54     220.69
2024-09-19    224.99    229.82   224.63     228.87
2024-09-20    229.97    233.09   227.62     228.20
2024-09-23    227.34    229.45   225.81     226.47
2024-09-24    228.65    229.35   225.73     227.37
2024-09-25    224.93    227.29   224.02     226.37
2024-09-26    227.30    228.50   225.41     227.52
2024-09-27    228.46    229.52   227.30     227.79

Selecting data for the last n days

# select the last day
last(AAPL,"1 day")

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2024-09-27    228.46    229.52    227.3     227.79

# select the last 3 days
last(AAPL,"3 day")

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2024-09-25    224.93    227.29   224.02     226.37
2024-09-26    227.30    228.50   225.41     227.52
2024-09-27    228.46    229.52   227.30     227.79

From daily price to weekly price

to.weekly(AAPL)

           AAPL.Open AAPL.High AAPL.Low AAPL.Close
2020-01-03   74.0600   75.1500  73.7975    74.3575
2020-01-10   73.4475   78.1675  73.1875    77.5825
2020-01-17   77.9100   79.6850  77.3875    79.6825
2020-01-24   79.2975   80.8325  78.9125    79.5775
2020-01-31   77.5150   81.9625  76.2200    77.3775
2020-02-07   76.0750   81.3050  75.5550    80.0075
2020-02-14   78.5450   81.8050  78.4625    81.2375
2020-02-21   78.8400   81.1625  77.6250    78.2625
2020-02-28   74.3150   76.0450  64.0925    68.3400
2020-03-06   70.5700   76.0000  69.4300    72.2575
       ...                                        
2024-07-26  227.0100  227.7800 214.6200   217.9600
2024-08-02  216.9600  225.6000 215.7500   219.8600
2024-08-09  199.0900  216.7800 196.0000   216.2400
2024-08-16  216.0700  226.8300 215.6000   226.0500
2024-08-23  225.7200  228.3400 223.0400   226.8400
2024-08-30  226.7600  232.9200 223.8900   229.0000
2024-09-06  228.5500  229.0000 217.4800   220.8200
2024-09-13  220.8200  224.0400 216.7100   222.5000
2024-09-20  216.5400  233.0900 213.9200   228.2000
2024-09-27  227.3400  229.5200 224.0200   227.7900

From daily price to monthly price

to.monthly(AAPL)

         AAPL.Open AAPL.High AAPL.Low AAPL.Close
Jan 2020   74.0600   81.9625  73.1875    77.3775
Feb 2020   76.0750   81.8050  64.0925    68.3400
Mar 2020   70.5700   76.0000  53.1525    63.5725
Apr 2020   61.6250   73.6325  59.2250    73.4500
May 2020   71.5625   81.0600  71.4625    79.4850
Jun 2020   79.4375   93.0950  79.3025    91.2000
Jul 2020   91.2800  106.4150  89.1450   106.2600
Aug 2020  108.2000  131.0000 107.8925   129.0400
Sep 2020  132.7600  137.9800 103.1000   115.8100
Oct 2020  117.6400  125.3900 107.7200   108.8600
Nov 2020  109.1100  121.9900 107.3200   119.0500
Dec 2020  121.0100  138.7900 120.0100   132.6900
Jan 2021  133.5200  145.0900 126.3800   131.9600
Feb 2021  133.7500  137.8800 118.3900   121.2600
Mar 2021  123.7500  128.7200 116.2100   122.1500
Apr 2021  123.6600  137.0700 122.4900   131.4600
May 2021  132.0400  134.0700 122.2500   124.6100
Jun 2021  125.0800  137.4100 123.1300   136.9600
Jul 2021  136.6000  150.0000 135.7600   145.8600
Aug 2021  146.3600  153.4900 144.5000   151.8300
Sep 2021  152.8300  157.2600 141.2700   141.5000
Oct 2021  141.9000  153.1700 138.2700   149.8000
Nov 2021  148.9900  165.7000 147.4800   165.3000
Dec 2021  167.4800  182.1300 157.8000   177.5700
Jan 2022  177.8300  182.9400 154.7000   174.7800
Feb 2022  174.0100  176.6500 152.0000   165.1200
Mar 2022  164.7000  179.6100 150.1000   174.6100
Apr 2022  174.0300  178.4900 155.3800   157.6500
May 2022  156.7100  166.4800 132.6100   148.8400
Jun 2022  149.9000  151.7400 129.0400   136.7200
Jul 2022  136.0400  163.6300 135.6600   162.5100
Aug 2022  161.0100  176.1500 157.1400   157.2200
Sep 2022  156.6400  164.2600 138.0000   138.2000
Oct 2022  138.2100  157.5000 134.3700   153.3400
Nov 2022  155.0800  155.4500 134.3800   148.0300
Dec 2022  148.2100  150.9200 125.8700   129.9300
Jan 2023  130.2800  147.2300 124.1700   144.2900
Feb 2023  143.9700  157.3800 141.3200   147.4100
Mar 2023  146.8300  165.0000 143.9000   164.9000
Apr 2023  164.2700  169.8500 159.7800   169.6800
May 2023  169.2800  179.3500 164.3100   177.2500
Jun 2023  177.7000  194.4800 176.9300   193.9700
Jul 2023  193.7800  198.2300 186.6000   196.4500
Aug 2023  196.2400  196.7300 171.9600   187.8700
Sep 2023  189.4900  189.9800 167.6200   171.2100
Oct 2023  171.2200  182.3400 165.6700   170.7700
Nov 2023  171.0000  192.9300 170.1200   189.9500
Dec 2023  190.3300  199.6200 187.4500   192.5300
Jan 2024  187.1500  196.3800 180.1700   184.4000
Feb 2024  183.9900  191.0500 179.2500   180.7500
Mar 2024  179.5500  180.5300 168.4900   171.4800
Apr 2024  171.1900  178.3600 164.0800   170.3300
May 2024  169.5800  193.0000 169.1100   192.2500
Jun 2024  192.9000  220.2000 192.1500   210.6200
Jul 2024  212.0900  237.2300 211.9200   222.0800
Aug 2024  224.3700  232.9200 196.0000   229.0000
Sep 2024  228.5500  233.0900 213.9200   227.7900

From daily price to monthly price

to.quarterly(AAPL)

        AAPL.Open AAPL.High AAPL.Low AAPL.Close
2020 Q1    74.060   81.9625  53.1525    63.5725
2020 Q2    61.625   93.0950  59.2250    91.2000
2020 Q3    91.280  137.9800  89.1450   115.8100
2020 Q4   117.640  138.7900 107.3200   132.6900
2021 Q1   133.520  145.0900 116.2100   122.1500
2021 Q2   123.660  137.4100 122.2500   136.9600
2021 Q3   136.600  157.2600 135.7600   141.5000
2021 Q4   141.900  182.1300 138.2700   177.5700
2022 Q1   177.830  182.9400 150.1000   174.6100
2022 Q2   174.030  178.4900 129.0400   136.7200
2022 Q3   136.040  176.1500 135.6600   138.2000
2022 Q4   138.210  157.5000 125.8700   129.9300
2023 Q1   130.280  165.0000 124.1700   164.9000
2023 Q2   164.270  194.4800 159.7800   193.9700
2023 Q3   193.780  198.2300 167.6200   171.2100
2023 Q4   171.220  199.6200 165.6700   192.5300
2024 Q1   187.150  196.3800 168.4900   171.4800
2024 Q2   171.190  220.2000 164.0800   210.6200
2024 Q3   212.090  237.2300 196.0000   227.7900

How to export XTS object to xlsx file

• Case 1
• Case 2

Case 1: Export only one xts object to an .xlsx file.

Step 1: Convert the xts object to a data frame.

DF_AAPL <- AAPL |> 
                 coredata() |> 
                 as.data.frame()
DF_AAPL$Date <- index(AAPL)

• coredata() extracts the data values from the xts object, excluding the time index.

• as.data.frame() converts the extracted data into a standard data frame.

• DF_AAPL$Date <- index(AAPL) adds a new column called Date, containing the time index from the original xts object.

Step 2: Save data frame DF_AAP to xlsx

library(writexl)
write_xlsx(DF_AAPL, "AAPL.xlsx")

Case 2: Export multiple xts objects to an .xlsx file.

Step 1: Convert the xts object to a data frame.

DF_MSFT <- MSFT |> 
                 coredata() |> 
                 as.data.frame()
DF_MSFT$Date <- index(MSFT)
DF_META <- META |> 
                 coredata() |> 
                 as.data.frame()
DF_META$Date <- index(META)

Step 2: Save every data frame to xlsx

library(writexl)
write_xlsx(list(DF_AAPL, DF_META, DF_MSFT), "stock.xlsx")

How to Import Time Series Data from an XLSX File into an XTS Object in R

You can download financial time series data from my webapp.

Assume we have excel file file.xlsx

library(readxl)
# TS = time series data frame
TS <- read_excel("file.xlsx", 
    col_types = c("date", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric"))
str(TS)

library(readxl)
# TS = time series data frame
TS <- read_excel("file.xlsx", 
    col_types = c("date", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric"))
str(TS)

tibble [968 × 7] (S3: tbl_df/tbl/data.frame)
 $ date           : POSIXct[1:968], format: "2021-01-04" "2021-01-05" ...
 $ PTT.BK.Open    : num [1:968] 41 42.2 43 43 42.8 ...
 $ PTT.BK.High    : num [1:968] 42.8 42.5 43.5 43.2 43.5 ...
 $ PTT.BK.Low     : num [1:968] 40.8 41.2 42.5 42.2 42 ...
 $ PTT.BK.Close   : num [1:968] 42.5 42 42.8 42.5 42.8 ...
 $ PTT.BK.Volume  : num [1:968] 74638600 75867500 88565400 63128900 83698400 ...
 $ PTT.BK.Adjusted: num [1:968] 33 32.6 33.1 33 33.1 ...

Convert the data frame TS to an XTS object using this function.

library(xts)
TS <- xts(x = TS[,2:6],
          order.by = TS$date)
first(TS, n = 7)

library(xts)
TS <- xts(x = TS[,2:6],
          order.by = TS$date)
first(TS,n = 7)

           PTT.BK.Open PTT.BK.High PTT.BK.Low PTT.BK.Close PTT.BK.Volume
2021-01-04       41.00       42.75      40.75        42.50      74638600
2021-01-05       42.25       42.50      41.25        42.00      75867500
2021-01-06       43.00       43.50      42.50        42.75      88565400
2021-01-07       43.00       43.25      42.25        42.50      63128900
2021-01-08       42.75       43.50      42.00        42.75      83698400
2021-01-11       42.75       43.00      42.50        43.00      45553200
2021-01-12       43.00       44.00      42.50        43.75     101081900

Exercise

Exercise 1: Extract Data for a Specific Year

Extract and display the stock data for “MSFT” for the year 2021. Use the xts package’s subsetting techniques to filter by date.

Solution.

Solution:

   msft_2021 <- MSFT["2021"]
   head(msft_2021)

Exercise 2: Filter Data Between Two Dates

Subset the “META” data to include only the observations between January 1, 2022, and December 31, 2022.

Solution.

Solution:

   meta_2022 <- META["2022-01-01/2022-12-31"]
   head(meta_2022)

Exercise 3: Select Data for the First Quarter

Extract the data for “MSFT” for the first quarter of 2023 (January to March) and display the results.

Solution.

Solution:

   msft_q1_2023 <- MSFT["2023-01/2023-03"]
   head(msft_q1_2023)

Exercise 4: Subset Data for the First N Weeks

Create a subset of “META” data for the first 10 weeks of 2024 and calculate the average closing price during that period.

Solution.

Solution:

mean(META$META.Close["2024"])

Exercise 5: Compare Data Over Two Periods

Extract “MSFT” data for the first 6 months of 2021 and 2023, and compare the average closing prices for these two periods.

Solution.

Solution:

   avg_2021 <- mean(MSFT$MSFT.Close["2021-01/2021-06"])
   avg_2023 <- mean(MSFT$MSFT.Close["2023-01/2023-06"])
   c(avg_2021, avg_2023)

Exercise 6: Select the Last N Observations

Subset the “META” data to get the last 8 weeks of observations before September 30, 2024.

Solution.

Solution:

tail(META["/2024-09-30"], n=8)